\documentclass[11pt,a4paper,]{} \usepackage{lmodern} \usepackage{amssymb,amsmath} \usepackage{ifxetex,ifluatex} \usepackage{fixltx2e} % provides \textsubscript \ifnum 0\ifxetex 1\fi\ifluatex 1\fi=0 % if pdftex \usepackage[T1]{fontenc} \usepackage[utf8]{inputenc} \else % if luatex or xelatex \usepackage{unicode-math} \defaultfontfeatures{Ligatures=TeX,Scale=MatchLowercase} \fi % use upquote if available, for straight quotes in verbatim environments \IfFileExists{upquote.sty}{\usepackage{upquote}}{} % use microtype if available \IfFileExists{microtype.sty}{% \usepackage[]{microtype} \UseMicrotypeSet[protrusion]{basicmath} % disable protrusion for tt fonts }{} \PassOptionsToPackage{hyphens}{url} % url is loaded by hyperref \usepackage[unicode=true]{hyperref} \hypersetup{ pdfborder={0 0 0}, breaklinks=true} \urlstyle{same} % don't use monospace font for urls \usepackage{geometry} \geometry{a4paper, centering, text={16cm,24cm}} \IfFileExists{parskip.sty}{% \usepackage{parskip} }{% else 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\usepackage[showonlyrefs]{mathtools} \usepackage[no-weekday]{eukdate} %% BIBLIOGRAPHY \makeatletter \@ifpackageloaded{biblatex}{}{\usepackage[style=authoryear-comp, backend=biber, natbib=true]{biblatex}} \makeatother \ExecuteBibliographyOptions{bibencoding=utf8,minnames=1,maxnames=3, maxbibnames=99,dashed=false,terseinits=true,giveninits=true,uniquename=false,uniquelist=false,doi=false, isbn=false,url=true,sortcites=false} \DeclareFieldFormat{url}{\texttt{\url{#1}}} \DeclareFieldFormat[article]{pages}{#1} \DeclareFieldFormat[inproceedings]{pages}{\lowercase{pp.}#1} \DeclareFieldFormat[incollection]{pages}{\lowercase{pp.}#1} \DeclareFieldFormat[article]{volume}{\mkbibbold{#1}} \DeclareFieldFormat[article]{number}{\mkbibparens{#1}} \DeclareFieldFormat[article]{title}{\MakeCapital{#1}} \DeclareFieldFormat[article]{url}{} %\DeclareFieldFormat[book]{url}{} %\DeclareFieldFormat[inbook]{url}{} %\DeclareFieldFormat[incollection]{url}{} %\DeclareFieldFormat[inproceedings]{url}{} \DeclareFieldFormat[inproceedings]{title}{#1} \DeclareFieldFormat{shorthandwidth}{#1} %\DeclareFieldFormat{extrayear}{} % No dot before number of articles \usepackage{xpatch} \xpatchbibmacro{volume+number+eid}{\setunit*{\adddot}}{}{}{} % Remove In: for an article. \renewbibmacro{in:}{% \ifentrytype{article}{}{% \printtext{\bibstring{in}\intitlepunct}}} \AtEveryBibitem{\clearfield{month}} \AtEveryCitekey{\clearfield{month}} \makeatletter \DeclareDelimFormat[cbx@textcite]{nameyeardelim}{\addspace} \makeatother \author{\sf\Large\textbf{ Mohammed Faizan}\\ {\sf\large MBAt\\[0.5cm]} \sf\Large\textbf{ Adarsh More}\\ {\sf\large MBAt\\[0.5cm]} \sf\Large\textbf{ Yanhui LI}\\ {\sf\large MBAt\\[0.5cm]}} \date{\sf\Date~\Month~\Year} \makeatletter \lfoot{\sf Faizan, More, LI: \@date} \makeatother %%%% PAGE STYLE FOR FRONT PAGE OF REPORTS \makeatletter \def\organization#1{\gdef\@organization{#1}} \def\telephone#1{\gdef\@telephone{#1}} \def\email#1{\gdef\@email{#1}} \makeatother \organization{Monash University} \def\name{Our consultancy - Star WarsMohammed Faizan &Adarsh More&Yanhui LI} \telephone{(03) 9905 2478} \email{questions@company.com} %NEW: New email addresss \def\webaddress{\url{http://company.com/stats/consulting/}} %NEW: URl \def\abn{12 377 614 630} % NEW: ABN \def\logo{\includegraphics[width=6cm]{Figures/logo}} %NEW: Changing logo \def\extraspace{\vspace*{1.6cm}} \makeatletter \def\contactdetails{\faicon{phone} & \@telephone \\ \faicon{envelope} & \@email} \makeatother %%%% FRONT PAGE OF REPORTS \def\reporttype{Report for} \long\def\front#1#2#3{ \newpage \begin{singlespacing} \thispagestyle{empty} \vspace*{-1.4cm} \hspace*{-1.4cm} \hbox to 16cm{ \hbox to 6.5cm{\vbox to 14cm{\vbox to 25cm{ \logo \vfill \parbox{6.3cm}{\raggedright \sf\color[rgb]{0.8, 0.7, 0.1 } % NEW color {\large\textbf{\name}}\par \vspace{.7cm} \tabcolsep=0.12cm\sf\small \begin{tabular}{@{}ll@{}}\contactdetails \end{tabular} \vspace*{0.3cm}\par ABN: \abn\par } }\vss}\hss} \hspace*{0.2cm} \hbox to 1cm{\vbox to 14cm{\rule{4pt}{26.8cm}\vss}\hss\hfill} %NEW: Thicker line \hbox to 10cm{\vbox to 14cm{\vbox to 25cm{ \vspace*{3cm}\sf\raggedright \parbox{11cm}{\sf\raggedright\baselineskip=1.2cm \fontsize{24.88}{30}\color[rgb]{0, 0.29, 0.55}\sf\textbf{#1}} % NEW: title color blue \par \vfill \large \vbox{\parskip=0.8cm #2}\par \vspace*{2cm}\par \reporttype\\[0.3cm] \hbox{#3}%\\[2cm]\ \vspace*{1cm} {\large\sf\textbf{\Date~\Month~\Year}} }\vss} }} \end{singlespacing} \newpage } \makeatletter \def\titlepage{\front{\expandafter{\@title}}{\@author}{\@organization}} \makeatother \usepackage{setspace} \setstretch{1.5} <<<<<<< HEAD %% Any special functions or other packages can be loaded here. \AtBeginDocument{\addtocontents{toc}{\protect\thispagestyle{empty}}} \usepackage{capt-of} \usepackage{graphicx} \usepackage{url} \usepackage{float} ======= >>>>>>> fc57fe83b5555588ff478f7a4d72bf7679cd7c6a \begin{document} \titlepage <<<<<<< HEAD <<<<<<< HEAD ======= ======= { \setcounter{tocdepth}{} \tableofcontents }
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Education level count by gender

(#fig:edu_gender)Education level count by gender

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Population distribution of education level

Figure 1: Population distribution of education level

Best education level of each region

Figure 2: Best education level of each region

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Population distribution of field

Figure 3: Population distribution of field

Best field of each region

Figure 4: Best field of each region

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0.0.1 Population Map

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0.0.2 Population Table

Table 1: Victoriqn Population
SA4_CODE_2016 femalepopulation malepopulation population
201 32726 34691 67417
202 32396 34054 66450
203 60660 64307 124967
204 35934 39614 75548
205 52929 57572 110501
206 159362 160819 320181
207 81814 86786 168600
208 96482 101671 198153
209 109370 122195 231565
210 71224 85167 156391
211 118179 129501 247680
212 151481 184164 335645
213 147830 178340 326170
214 62731 68190 130921
215 29867 33492 63359
216 25915 28796 54711
217 26236 29297 55533
297 0 9 9
299 765 1229 1994

0.0.3 Age Distribution

0.0.4 Population by Education

0.0.5 Population by Industry

0.0.6 Population by Field

0.0.7 Population by Occupation

0.0.8 Occupation: Male vs Female

0.0.9 Occupation: Male vs Female

0.0.10 Industry: Male vs Female

0.0.11 Industry: Male vs Female

0.0.12 Population by Education

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0.0.13 Population by Industries

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0.0.14 Population by Field

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Spatial Industry Distribution

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0.0.15 Population by Occupation

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0.0.16 Population by Education, Age

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Table 2: Education: Population
afq_level age_min population
Level 1 & 2 15 9402
Level 3 & 4 25 146297
Level 5 & 6 25 96920
Level 7 25 245613
Level 9 25 83204
Not Stated 25 70455
Level 8 35 28908

0.0.17 Population by Industries, Age

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Table 3: Industry: Population
industry age_min population
Accommodation_and_food_services 25 42103
Administrative_and_support_services 25 23086
Arts_and_recreation_services 25 13149
Construction 25 61959
Electricity_gas_water_and_waste_service 25 8039
Financial_and_insurance_services 25 32021
Health_care_and_social_assistance 25 80994
Information_media_and_telecommunications 25 14702
Not Stated 25 29901
Other_services 25 24089
Professional_scientific_and_technical_services 25 64125
Rental_hiring_and_real_estate_services 25 11796
Retail_trade 25 61803
Mining 35 2441
Wholesale_trade 35 22199
Education_and_training 45 56125
Manufacturing 45 55206
Public_administration_and_safety 45 37747
Transport_postal_and_warehousing 45 32663
Agriculture_forestry_and_fishing 55 12733

0.0.18 Population by Field

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Table 4: Field: Population
field age_min population
Mixed_Field_Programmes 15 1813
Architecture_and_Building 25 42510
Creative_Arts 25 40334
Food_Hospitality_and_Personal_Services 25 42938
Health 25 67630
Information_Technology 25 37535
Management_and_Commerce 25 150571
Natural_and_Physical_Sciences 25 22171
Not Stated 25 71440
Society_and_Culture 25 80932
Agriculture_Environment 35 13016
Engineering_and_Technologies 45 77524
Education 55 44696
NA NA 896

0.0.19 Population by Occupation

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Table 5: Occupation: Population
occupation age_min population
Community_and_personal_service_workers 25 67104
Not Stated 25 11075
Professionals 25 190449
Sales_workers 25 51772
Technicians_and_trades_workers 25 99110
Managers 35 100601
Clerical_and_administrative_workers 45 89021
Labourers 45 49653
Machinery_operators_and_drivers 45 40922

0.0.20 Education Level: Region

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Best education level of each region

Figure 1: Best education level of each region

0.0.21 Industry: Region

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0.0.22 Field: Region

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Best field of each region

Figure 2: Best field of each region

0.0.23 Occupation: Region

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0.0.24 Education Level: Region

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Spatial Education Level Distribution

Figure 3: Spatial Education Level Distribution

0.0.25 Industry: Region

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Spatial Industry Distribution

Figure 4: Spatial Industry Distribution

0.0.26 Field: Region

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Spatial Study Field Distribution

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0.0.27 Occupation: Region

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Spatial Occupation Distribution

Figure 6: Spatial Occupation Distribution

0.0.28 Education Level: Region

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0.0.29 Industry: Region

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0.0.30 Field: Region

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0.0.31 Occupation: Region

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0.0.32 Chart A

0.0.33 Chart B

0.0.34 Chart C

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It can be observed from fig @ref(fig:hr_plots) that overall females worked more than men. However, as the number of work-hours increased men have worked more than women.

It can be observed from fig @ref(fig:ind_hrs) that industries like health care, education and training, construction and Professional and technical services have more working population as the working hours increased. Mining, electricity, gas, water and agriculture forestry and fishing showed low working population irrespective of work hours.

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It can be observed from fig @ref(fig:hrs_plots) that overall females worked more than men at all occupations. Although, for maximum hours worked, as number of working-hours increased, the number of men and women remained the same.

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It can be observed from fig ?? tha the most number of employees in the SA4 regions are employed in the occupations of Professionals, Managers and Technicians and trade workers. Professionals accounted for highest number of employees for region 206, while machinery operators and drivers accounted for the least number of employees for region 213 respectively.

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Conclusion

The education levels, field of study, industry of employment and occupation was studied for the Victorian SA4 level populations for the distributions according to gender and sex. The tables and plots were compared to mark the covariations between the population distributions.For example, the population trend between the field of study and industry of employment. Networks were drawn based on the population weights to analyze these trends. Some of the trends like more men were employed as managers when more women had studied management were found to be interesting. Cholropeth maps were made to analyze these trends spatially.

The goal of this report is to create a data story from these statistical summaries to enumerate the facts from the data and link them to the real world. The data provided by the Australian Bureau of Statistics is an aggregated open data and in no form identifies individuals who participated in the census. The ABS aims to integrate the census data with other datasets to make this census data more interesting. Thus, we aim to do the same and bring some interesting data stories as we progress building this report.

R Core Team (2021)

Xie (2021a) Dietrich (2020) Wickham et al. (2021),

Wickham (2021a),

Wickham et al. (2020),

Zhu (2021),

Xie (2021b),

Tierney et al. (2020),

Pedersen (2020),

Henry and Wickham (2020),

Wickham and Hester (2020),

Wickham and Seidel (2020),

Wickham (2019),

Müller and Wickham (2021),

Wickham (2021b),

Wickham (2021c),

Xie (2021c),

Tierney (2019),

Xie (2016),

Wickham (2016),

Xie (2015),

Xie (2014),

Wickham et al. (2019),

Xie (2019),

Tierney (2017)

Dietrich, Jan Philipp. 2020. Citation: Software Citation Tools. https://CRAN.R-project.org/package=citation.
Henry, Lionel, and Hadley Wickham. 2020. Purrr: Functional Programming Tools. https://CRAN.R-project.org/package=purrr.
Müller, Kirill, and Hadley Wickham. 2021. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
Pedersen, Thomas Lin. 2020. Patchwork: The Composer of Plots. https://CRAN.R-project.org/package=patchwork.
R Core Team. 2021. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Tierney, Nicholas. 2017. “Visdat: Visualising Whole Data Frames.” JOSS 2 (16): 355. https://doi.org/10.21105/joss.00355.
———. 2019. Visdat: Preliminary Visualisation of Data. https://CRAN.R-project.org/package=visdat.
Tierney, Nicholas, Di Cook, Miles McBain, and Colin Fay. 2020. Naniar: Data Structures, Summaries, and Visualisations for Missing Data. https://github.com/njtierney/naniar.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.
———. 2019. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
———. 2021a. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats.
———. 2021b. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.
———. 2021c. Tidyverse: Easily Install and Load the Tidyverse. https://CRAN.R-project.org/package=tidyverse.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, and Dewey Dunnington. 2020. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2021. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, and Jim Hester. 2020. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.
Wickham, Hadley, and Dana Seidel. 2020. Scales: Scale Functions for Visualization. https://CRAN.R-project.org/package=scales.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595.
———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
———. 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://bookdown.org/yihui/bookdown.
———. 2019. “TinyTeX: A Lightweight, Cross-Platform, and Easy-to-Maintain LaTeX Distribution Based on TeX Live.” TUGboat, no. 1: 30–32. http://tug.org/TUGboat/Contents/contents40-1.html.
———. 2021a. Bookdown: Authoring Books and Technical Documents with r Markdown. https://CRAN.R-project.org/package=bookdown.
———. 2021b. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.
———. 2021c. Tinytex: Helper Functions to Install and Maintain TeX Live, and Compile LaTeX Documents. https://github.com/yihui/tinytex.
Zhu, Hao. 2021. kableExtra: Construct Complex Table with Kable and Pipe Syntax. https://CRAN.R-project.org/package=kableExtra.
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